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Paper 1

The Role of Community Building and Education as Key Pillar of Institutionalizing Responsible Quantum

Sanjay Vishwakarma, Vishal Sharathchandra Bajpe, Ryan Mandelbaum, Yuri Kobayashi, Olivia Lanes, Mira Luca Wolf-Bauwens

Year

2024

Journal

arXiv preprint

DOI

arXiv:2410.17285

arXiv

2410.17285

Quantum computing is an emerging technology whose positive and negative impacts on society are not yet fully known. As government, individuals, institutions, and corporations fund and develop this technology, they must ensure that they anticipate its impacts, prepare for its consequences, and steer its development in such a way that it enables the most good and prevents the most harm. However, individual stakeholders are not equipped to fully anticipate these consequences on their own it requires a diverse community that is well-informed about quantum computing and its impacts. Collaborations and community-building across domains incorporating a variety of viewpoints, especially those from stakeholders most likely to be harmed, are fundamental pillars of developing and deploying quantum computing responsibly. This paper reviews responsible quantum computing proposals and literature, highlights the challenges in implementing these, and presents strategies developed at IBM aimed at building a diverse community of users and stakeholders to support the responsible development of this technology.

Open paper

Paper 2

Quantum Monte Carlo Integration for Simulation-Based Optimisation

Jingjing Cui, Philippe J. S. de Brouwer, Steven Herbert, Philip Intallura, Cahit Kargi, Georgios Korpas, Alexandre Krajenbrink, William Shoosmith, Ifan Williams, Ban Zheng

Year

2024

Journal

arXiv preprint

DOI

arXiv:2410.03926

arXiv

2410.03926

We investigate the feasibility of integrating quantum algorithms as subroutines of simulation-based optimisation problems with relevance to and potential applications in mathematical finance. To this end, we conduct a thorough analysis of all systematic errors arising in the formulation of quantum Monte Carlo integration in order to better understand the resources required to encode various distributions such as a Gaussian, and to evaluate statistical quantities such as the Value-at-Risk (VaR) and Conditional-Value-at-Risk (CVaR) of an asset. Finally, we study the applicability of quantum Monte Carlo integration for fundamental financial use cases in terms of simulation-based optimisations, notably Mean-Conditional-Value-at-Risk (Mean-CVaR) and (risky) Mean-Variance (Mean-Var) optimisation problems. In particular, we study the Mean-Var optimisation problem in the presence of noise on a quantum device, and benchmark a quantum error mitigation method that applies to quantum amplitude estimation -- a key subroutine of quantum Monte Carlo integration -- showcasing the utility of such an approach.

Open paper